A note on estimating human emotion evoked by visual stimuli using fNIRS signals

Abstract

This paper presents a method that estimates human emotion evoked by visual stimuli using functional near-infrared spectroscopy(fNIRS) signals. The proposed method enables estimation of individual emotion based on feature extraction and machine learning methodology. In our method, Fisher score-based supervised feature selection and successively orthogonal discriminant analysis (SODA)-based supervised dimensionality reduction are applied to fNIRS features extracted from fNIRS signals. Fisher score enables selection of effective features, i.e., channel, for estimating human emotion. Then SODA obtains transformed features that consider the relationship between the effective feature and the emotion evoked by visual stimuli. The performance improvement of emotion estimation can be expected by using the obtained features. Experimental results obtained by applying our method to actual fNIRS signals show its effectiveness.